from pathlib import Path from typing import Optional from PIL import Image from PIL.ImageOps import exif_transpose from torch.utils.data import Dataset from torchvision import transforms import json import random from facenet_pytorch import MTCNN import torch from utils.utils import extract_faces_and_landmarks, REFERNCE_FACIAL_POINTS_RELATIVE def load_image(image_path: str) -> Image: image = Image.open(image_path) image = exif_transpose(image) if not image.mode == "RGB": image = image.convert("RGB") return image class ImageDataset(Dataset): """ A dataset to prepare the instance and class images with the prompts for fine-tuning the model. It pre-processes the images. """ def __init__( self, instance_data_root, instance_prompt, metadata_path: Optional[str] = None, prompt_in_filename=False, use_only_vanilla_for_encoder=False, concept_placeholder='a face', size=1024, center_crop=False, aug_images=False, use_only_decoder_prompts=False, crop_head_for_encoder_image=False, random_target_prob=0.0, ): self.mtcnn = MTCNN(device='cuda:0') self.mtcnn.forward = self.mtcnn.detect resize_factor = 1.3 self.resized_reference_points = REFERNCE_FACIAL_POINTS_RELATIVE / resize_factor + (resize_factor - 1) / (2 * resize_factor) self.size = size self.center_crop = center_crop self.concept_placeholder = concept_placeholder self.prompt_in_filename = prompt_in_filename self.aug_images = aug_images self.instance_prompt = instance_prompt self.custom_instance_prompts = None self.name_to_label = None self.crop_head_for_encoder_image = crop_head_for_encoder_image self.random_target_prob = random_target_prob self.use_only_decoder_prompts = use_only_decoder_prompts self.instance_data_root = Path(instance_data_root) if not self.instance_data_root.exists(): raise ValueError(f"Instance images root {self.instance_data_root} doesn't exist.") if metadata_path is not None: with open(metadata_path, 'r') as f: self.name_to_label = json.load(f) # dict of filename: label # Create a reversed mapping self.label_to_names = {} for name, label in self.name_to_label.items(): if use_only_vanilla_for_encoder and 'vanilla' not in name: continue if label not in self.label_to_names: self.label_to_names[label] = [] self.label_to_names[label].append(name) self.all_paths = [self.instance_data_root / filename for filename in self.name_to_label.keys()] # Verify all paths exist n_all_paths = len(self.all_paths) self.all_paths = [path for path in self.all_paths if path.exists()] print(f'Found {len(self.all_paths)} out of {n_all_paths} paths.') else: self.all_paths = [path for path in list(Path(instance_data_root).glob('**/*')) if path.suffix.lower() in [".png", ".jpg", ".jpeg"]] # Sort by name so that order for validation remains the same across runs self.all_paths = sorted(self.all_paths, key=lambda x: x.stem) self.custom_instance_prompts = None self._length = len(self.all_paths) self.class_data_root = None self.image_transforms = transforms.Compose( [ transforms.Resize(size, interpolation=transforms.InterpolationMode.BILINEAR), transforms.CenterCrop(size) if center_crop else transforms.RandomCrop(size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5]), ] ) if self.prompt_in_filename: self.prompts_set = set([self._path_to_prompt(path) for path in self.all_paths]) else: self.prompts_set = set([self.instance_prompt]) if self.aug_images: self.aug_transforms = transforms.Compose( [ transforms.RandomResizedCrop(size, scale=(0.8, 1.0), ratio=(1.0, 1.0)), transforms.RandomHorizontalFlip(p=0.5) ] ) def __len__(self): return self._length def _path_to_prompt(self, path): # Remove the extension and seed split_path = path.stem.split('_') while split_path[-1].isnumeric(): split_path = split_path[:-1] prompt = ' '.join(split_path) # Replace placeholder in prompt with training placeholder prompt = prompt.replace('conceptname', self.concept_placeholder) return prompt def __getitem__(self, index): example = {} instance_path = self.all_paths[index] instance_image = load_image(instance_path) example["instance_images"] = self.image_transforms(instance_image) if self.prompt_in_filename: example["instance_prompt"] = self._path_to_prompt(instance_path) else: example["instance_prompt"] = self.instance_prompt if self.name_to_label is None: # If no labels, simply take the same image but with different augmentation example["encoder_images"] = self.aug_transforms(example["instance_images"]) if self.aug_images else example["instance_images"] example["encoder_prompt"] = example["instance_prompt"] else: # Randomly select another image with the same label instance_name = str(instance_path.relative_to(self.instance_data_root)) instance_label = self.name_to_label[instance_name] label_set = set(self.label_to_names[instance_label]) if len(label_set) == 1: # We are not supposed to have only one image per label, but just in case encoder_image_name = instance_name print(f'WARNING: Only one image for label {instance_label}.') else: encoder_image_name = random.choice(list(label_set - {instance_name})) encoder_image = load_image(self.instance_data_root / encoder_image_name) example["encoder_images"] = self.image_transforms(encoder_image) if self.prompt_in_filename: example["encoder_prompt"] = self._path_to_prompt(self.instance_data_root / encoder_image_name) else: example["encoder_prompt"] = self.instance_prompt if self.crop_head_for_encoder_image: example["encoder_images"] = extract_faces_and_landmarks(example["encoder_images"][None], self.size, self.mtcnn, self.resized_reference_points)[0][0] example["encoder_prompt"] = example["encoder_prompt"].format(placeholder="") example["instance_prompt"] = example["instance_prompt"].format(placeholder="") if random.random() < self.random_target_prob: random_path = random.choice(self.all_paths) random_image = load_image(random_path) example["instance_images"] = self.image_transforms(random_image) if self.prompt_in_filename: example["instance_prompt"] = self._path_to_prompt(random_path) if self.use_only_decoder_prompts: example["encoder_prompt"] = example["instance_prompt"] return example def collate_fn(examples, with_prior_preservation=False): pixel_values = [example["instance_images"] for example in examples] encoder_pixel_values = [example["encoder_images"] for example in examples] prompts = [example["instance_prompt"] for example in examples] encoder_prompts = [example["encoder_prompt"] for example in examples] if with_prior_preservation: raise NotImplementedError("Prior preservation not implemented.") pixel_values = torch.stack(pixel_values) pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() encoder_pixel_values = torch.stack(encoder_pixel_values) encoder_pixel_values = encoder_pixel_values.to(memory_format=torch.contiguous_format).float() batch = {"pixel_values": pixel_values, "encoder_pixel_values": encoder_pixel_values, "prompts": prompts, "encoder_prompts": encoder_prompts} return batch